





import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
  from transformers.models.esm.modeling_esm import (
      ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
      EsmEmbeddings,
      create_position_ids_from_input_ids,
  )






class 							__lowercase			:






      def __init__(		self			,							lowercase_			,							lowercase_=1_3			,							lowercase_=7			,							lowercase_=False			,							lowercase_=True			,							lowercase_=False			,							lowercase_=True			,							lowercase_=3_3			,							lowercase_=3_2			,							lowercase_=5			,							lowercase_=4			,							lowercase_=3_7			,							lowercase_="gelu"			,							lowercase_=0.1			,							lowercase_=0.1			,							lowercase_=5_1_2			,							lowercase_=1_6			,							lowercase_=2			,							lowercase_=0.02			,							lowercase_=3			,							lowercase_=4			,							lowercase_=None			,							)     ->       List[str]:
       __snake_case         =			parent
       __snake_case         =			batch_size
       __snake_case         =			seq_length
       __snake_case         =			is_training
       __snake_case         =			use_input_mask
       __snake_case         =			use_token_type_ids
       __snake_case         =			use_labels
       __snake_case         =			vocab_size
       __snake_case         =			hidden_size
       __snake_case         =			num_hidden_layers
       __snake_case         =			num_attention_heads
       __snake_case         =			intermediate_size
       __snake_case         =			hidden_act
       __snake_case         =			hidden_dropout_prob
       __snake_case         =			attention_probs_dropout_prob
       __snake_case         =			max_position_embeddings
       __snake_case         =			type_vocab_size
       __snake_case         =			type_sequence_label_size
       __snake_case         =			initializer_range
       __snake_case         =			num_labels
       __snake_case         =			num_choices
       __snake_case         =			scope






      def 			_a						(		self)     ->       str:
       __snake_case         =			ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size)

       __snake_case         =			None
       if self.use_input_mask:
        __snake_case         =			random_attention_mask([self.batch_size, self.seq_length])

       __snake_case         =			None
       __snake_case         =			None
       __snake_case         =			None
       if self.use_labels:
        __snake_case         =			ids_tensor([self.batch_size]			,							self.type_sequence_label_size)
        __snake_case         =			ids_tensor([self.batch_size, self.seq_length]			,							self.num_labels)
        __snake_case         =			ids_tensor([self.batch_size]			,							self.num_choices)

       __snake_case         =			self.get_config()

       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






      def 			_a						(		self)     ->       str:
       return EsmConfig(
           vocab_size=self.vocab_size			,							hidden_size=self.hidden_size			,							pad_token_id=1			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							intermediate_size=self.intermediate_size			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							max_position_embeddings=self.max_position_embeddings			,							type_vocab_size=self.type_vocab_size			,							initializer_range=self.initializer_range			,							)






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       Optional[Any]:
       __snake_case         =			EsmModel(config=_UpperCAmelCase)
       model.to(_UpperCAmelCase)
       model.eval()
       __snake_case         =			model(_UpperCAmelCase			,							attention_mask=_UpperCAmelCase)
       __snake_case         =			model(_UpperCAmelCase)
       __snake_case         =			model(_UpperCAmelCase)

       self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size))
       self.parent.assertEqual(result.pooler_output.shape			,							(self.batch_size, self.hidden_size))






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       Any:
       __snake_case         =			EsmForMaskedLM(config=_UpperCAmelCase)
       model.to(_UpperCAmelCase)
       model.eval()
       __snake_case         =			model(_UpperCAmelCase			,							attention_mask=_UpperCAmelCase			,							labels=_UpperCAmelCase)
       self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.seq_length, self.vocab_size))






      def 			_a						(		self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)     ->       List[str]:
       __snake_case         =			self.num_labels
       __snake_case         =			EsmForTokenClassification(config=_UpperCAmelCase)
       model.to(_UpperCAmelCase)
       model.eval()
       __snake_case         =			model(_UpperCAmelCase			,							attention_mask=_UpperCAmelCase			,							labels=_UpperCAmelCase)
       self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.seq_length, self.num_labels))







      def 			_a						(		self)     ->       List[Any]:
       __snake_case         =			self.prepare_config_and_inputs()
       (
           (
           __snake_case	
       )   ,    (
           __snake_case	
       )   ,    (
           __snake_case	
       )   ,    (
           __snake_case	
       )   ,    (
           __snake_case	
       )   ,    (
           __snake_case	
       )   ,    	
       )         =			config_and_inputs
       __snake_case         =			{'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict






@require_torch
class 							__lowercase			(     snake_case__   ,							snake_case__   ,							unittest.TestCase       ):
      __UpperCAmelCase		      =						False

      __UpperCAmelCase		      =						(
          (
              EsmForMaskedLM,
              EsmModel,
              EsmForSequenceClassification,
              EsmForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      __UpperCAmelCase		      =						()
      __UpperCAmelCase		      =						(
          {
              '''feature-extraction''': EsmModel,
              '''fill-mask''': EsmForMaskedLM,
              '''text-classification''': EsmForSequenceClassification,
              '''token-classification''': EsmForTokenClassification,
              '''zero-shot''': EsmForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __UpperCAmelCase		      =						True






      def 			_a						(		self)     ->       Any:
       __snake_case         =			EsmModelTester(self)
       __snake_case         =			ConfigTester(self			,							config_class=_UpperCAmelCase			,							hidden_size=3_7)






      def 			_a						(		self)     ->       List[str]:
       self.config_tester.run_common_tests()






      def 			_a						(		self)     ->       List[Any]:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase)






      def 			_a						(		self)     ->       List[str]:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
        __snake_case         =			type
        self.model_tester.create_and_check_model(*_UpperCAmelCase)






      def 			_a						(		self)     ->       Optional[int]:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase)






      def 			_a						(		self)     ->       str:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase)






      @slow
      def 			_a						(		self)     ->       Optional[int]:
       for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __snake_case         =			EsmModel.from_pretrained(_UpperCAmelCase)
        self.assertIsNotNone(_UpperCAmelCase)






      def 			_a						(		self)     ->       str:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()[0]
       __snake_case         =			EsmEmbeddings(config=_UpperCAmelCase)

       __snake_case         =			torch.as_tensor([[1_2, 3_1, 1_3, model.padding_idx]])
       __snake_case         =			torch.as_tensor(
           [
               [
                   0 + model.padding_idx + 1,
                   1 + model.padding_idx + 1,
                   2 + model.padding_idx + 1,
                   model.padding_idx,
               ]
           ])
       __snake_case         =			create_position_ids_from_input_ids(_UpperCAmelCase			,							model.padding_idx)
       self.assertEqual(position_ids.shape			,							expected_positions.shape)
       self.assertTrue(torch.all(torch.eq(_UpperCAmelCase			,							_UpperCAmelCase)))






      def 			_a						(		self)     ->       str:
       __snake_case         =			self.model_tester.prepare_config_and_inputs()[0]
       __snake_case         =			EsmEmbeddings(config=_UpperCAmelCase)

       __snake_case         =			torch.empty(2			,							4			,							3_0)
       __snake_case         =			[
           0 + embeddings.padding_idx + 1,
           1 + embeddings.padding_idx + 1,
           2 + embeddings.padding_idx + 1,
           3 + embeddings.padding_idx + 1,
       ]
       __snake_case         =			torch.as_tensor([expected_single_positions, expected_single_positions])
       __snake_case         =			embeddings.create_position_ids_from_inputs_embeds(_UpperCAmelCase)
       self.assertEqual(position_ids.shape			,							expected_positions.shape)
       self.assertTrue(torch.all(torch.eq(_UpperCAmelCase			,							_UpperCAmelCase)))






      @unittest.skip('Esm does not support embedding resizing')
      def 			_a						(		self)     ->       Union[str, Any]:
       pass






      @unittest.skip('Esm does not support embedding resizing')
      def 			_a						(		self)     ->       Any:
       pass







      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
      def 			_a						(		self)     ->       Tuple:
       pass






@require_torch
class 							__lowercase			(     snake_case__       ):






      @slow
      def 			_a						(		self)     ->       Optional[int]:
       with torch.no_grad():
        __snake_case         =			EsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D')
        model.eval()
        __snake_case         =			torch.tensor([[0, 1, 2, 3, 4, 5]])
        __snake_case         =			model(_UpperCAmelCase)[0]

        __snake_case         =			3_3

        __snake_case         =			torch.Size((1, 6, vocab_size))
        self.assertEqual(output.shape			,							_UpperCAmelCase)

        __snake_case         =			torch.tensor(
            [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]])
        self.assertTrue(torch.allclose(output[:, :3, :3]			,							_UpperCAmelCase			,							atol=1e-4))







      @slow
      def 			_a						(		self)     ->       List[str]:
       with torch.no_grad():
        __snake_case         =			EsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D')
        model.eval()

        __snake_case         =			torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]])
        __snake_case         =			model(_UpperCAmelCase)[0]
        # compare the actual values for a slice.
        __snake_case         =			torch.tensor(
            [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]])
        self.assertTrue(torch.allclose(output[:, :3, :3]			,							_UpperCAmelCase			,							atol=1e-4))






'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    list[str] | None = None      ,					UpperCamelCase_    :    dict[str, float] | None = None      ,					UpperCamelCase_    :    bool = False      ,					)       ->	tuple[int, float, str]:
		snake_case__	     =cipher_alphabet or [chr(UpperCamelCase_			) for i in range(97      ,					123			)]

		# If the argument is None or the user provided an empty dictionary
		if not frequencies_dict:
				# Frequencies of letters in the english language (how much they show up)
				snake_case__	     ={
				    'a': 0.0_8_4_9_7,
				    'b': 0.0_1_4_9_2,
				    'c': 0.0_2_2_0_2,
				    'd': 0.0_4_2_5_3,
				    'e': 0.1_1_1_6_2,
				    'f': 0.0_2_2_2_8,
				    'g': 0.0_2_0_1_5,
				    'h': 0.0_6_0_9_4,
				    'i': 0.0_7_5_4_6,
				    'j': 0.0_0_1_5_3,
				    'k': 0.0_1_2_9_2,
				    'l': 0.0_4_0_2_5,
				    'm': 0.0_2_4_0_6,
				    'n': 0.0_6_7_4_9,
				    'o': 0.0_7_5_0_7,
				    'p': 0.0_1_9_2_9,
				    'q': 0.0_0_0_9_5,
				    'r': 0.0_7_5_8_7,
				    's': 0.0_6_3_2_7,
				    't': 0.0_9_3_5_6,
				    'u': 0.0_2_7_5_8,
				    'v': 0.0_0_9_7_8,
				    'w': 0.0_2_5_6_0,
				    'x': 0.0_0_1_5_0,
				    'y': 0.0_1_9_9_4,
				    'z': 0.0_0_0_7_7,
				}
		else:
				# Custom frequencies dictionary
				snake_case__	     =frequencies_dict

		if not case_sensitive:
				snake_case__	     =ciphertext.lower()

		# Chi squared statistic values
		snake_case__	     ={}

		# cycle through all of the shifts
		for shift in range(len(UpperCamelCase_			)			):
				snake_case__	     =''

				# decrypt the message with the shift
				for letter in ciphertext:
						try:
								# Try to index the letter in the alphabet
								snake_case__	     =(alphabet_letters.index(letter.lower()			) - shift) % len(
								    UpperCamelCase_			)
								decrypted_with_shift += (
								    alphabet_letters[new_key].upper()
								    if case_sensitive and letter.isupper()
								    else alphabet_letters[new_key]
								)
						except ValueError:
								# Append the character if it isn't in the alphabet
								decrypted_with_shift += letter

				snake_case__	     =0.0

				# Loop through each letter in the decoded message with the shift
				for letter in decrypted_with_shift:
						if case_sensitive:
								snake_case__	     =letter.lower()
								if letter in frequencies:
										# Get the amount of times the letter occurs in the message
										snake_case__	     =decrypted_with_shift.lower().count(UpperCamelCase_			)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										snake_case__	     =frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										snake_case__	     =((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value
						else:
								if letter.lower() in frequencies:
										# Get the amount of times the letter occurs in the message
										snake_case__	     =decrypted_with_shift.count(UpperCamelCase_			)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										snake_case__	     =frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										snake_case__	     =((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
				snake_case__	     =(
				    chi_squared_statistic,
				    decrypted_with_shift,
				)

		# Get the most likely cipher by finding the cipher with the smallest chi squared
		# statistic
		def chi_squared_statistic_values_sorting_key(UpperCamelCase_    :    int			) -> tuple[float, str]:
				return chi_squared_statistic_values[key]

		snake_case__	     =min(
		    UpperCamelCase_      ,					key=UpperCamelCase_      ,					)

		# Get all the data from the most likely cipher (key, decoded message)
		(
		    (
		    snake_case__
		)     ,	(
		    snake_case__
		)     ,	
		)	     =chi_squared_statistic_values[most_likely_cipher]

		# Return the data on the most likely shift
		return (
		    most_likely_cipher,
		    most_likely_cipher_chi_squared_value,
		    decoded_most_likely_cipher,
		)




